论文标题
使用最小延迟的服务功能链的在线学习备用服务功能链
Online Learning for Failure-aware Edge Backup of Service Function Chains with the Minimum Latency
论文作者
论文摘要
虚拟网络功能（VNF）已被广泛地部署在移动边缘计算（MEC）中，以灵活有效地为运行资源密集型应用程序的最终用户提供服务，这些应用程序可以进一步序列化以形成服务功能链（SFCS），从而提供自定义的网络服务。为了确保SFC的可用性，事实证明，将冗余SFC备份放在边缘以快速从任何故障中恢复。现有的研究在很大程度上忽略了SFC受欢迎程度，备份完整性和故障率对Edge服务器上SFC备份最佳部署的影响。在本文中，我们从最终用户和边缘系统的角度全面考虑备份SFC，以提供最低延迟的流行服务。为了克服未知的SFC普及和故障率以及已知的系统参数约束所带来的挑战，我们利用在线强盗学习技术来应对不确定性问题。将原始方法与贪婪策略相结合，我们提出了一种实时选择和部署（RTSD）算法。进行了广泛的仿真实验，以证明我们提出的算法的优越性。
Virtual network functions (VNFs) have been widely deployed in mobile edge computing (MEC) to flexibly and efficiently serve end users running resource-intensive applications, which can be further serialized to form service function chains (SFCs), providing customized networking services. To ensure the availability of SFCs, it turns out to be effective to place redundant SFC backups at the edge for quickly recovering from any failures. The existing research largely overlooks the influences of SFC popularity, backup completeness and failure rate on the optimal deployment of SFC backups on edge servers. In this paper, we comprehensively consider from the perspectives of both the end users and edge system to backup SFCs for providing popular services with the lowest latency. To overcome the challenges resulted from unknown SFC popularity and failure rate, as well as the known system parameter constraints, we take advantage of the online bandit learning technique to cope with the uncertainty issue. Combining the Prim-inspired method with the greedy strategy, we propose a Real-Time Selection and Deployment(RTSD) algorithm. Extensive simulation experiments are conducted to demonstrate the superiority of our proposed algorithms.
